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app.py

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+import os
+
+import gradio as gr
+import numpy as np
+from transformers import AutoModelForTokenClassification
+from datasets.features import ClassLabel
+from transformers import AutoProcessor
+from datasets import Features, Sequence, ClassLabel, Value, Array2D, Array3D
+import torch
+from datasets import load_metric
+from transformers import LayoutLMv3ForTokenClassification
+from transformers.data.data_collator import default_data_collator
+
+
+from transformers import AutoModelForTokenClassification
+from datasets import load_dataset
+from PIL import Image, ImageDraw, ImageFont
+
+
+processor = AutoProcessor.from_pretrained("microsoft/layoutlmv3-base", apply_ocr=True)
+model = AutoModelForTokenClassification.from_pretrained("layoutlmv3-finetuned-invoice")
+
+
+
+# load image example
+dataset = load_dataset("balabis/invoices", use_auth_token=True, split="test")
+Image.open(dataset[2]["image_path"]).convert("RGB").save("example1.png")
+Image.open(dataset[1]["image_path"]).convert("RGB").save("example2.png")
+Image.open(dataset[0]["image_path"]).convert("RGB").save("example3.png")
+# define id2label, label2color
+labels = dataset.features['ner_tags'].feature.names
+id2label = {v: k for v, k in enumerate(labels)}
+label2color = {
+    "INVOICE_NUM": 'blue',
+    "INVOICE_DATE": 'blue',
+    "BUYER": 'green',
+    "SELLER": 'orange',
+    "TOTAL_SUM": "blue",
+    "TOTAL_VAT": 'green',
+    "PAY_UNTIL": 'violet'
+  } 
+
+def unnormalize_box(bbox, width, height):
+     return [
+         width * (bbox[0] / 1000),
+         height * (bbox[1] / 1000),
+         width * (bbox[2] / 1000),
+         height * (bbox[3] / 1000),
+     ]
+
+
+def iob_to_label(label):
+    return label
+
+
+
+def process_image(image):
+    width, height = image.size
+    # encode
+    encoding = processor(image, truncation=True, return_offsets_mapping=True, return_tensors="pt")
+    offset_mapping = encoding.pop('offset_mapping')
+    # forward pass
+    outputs = model(**encoding)
+
+    # get predictions
+    predictions = outputs.logits.argmax(-1).squeeze().tolist()
+    token_boxes = encoding.bbox.squeeze().tolist()
+    # only keep non-subword predictions
+    is_subword = np.array(offset_mapping.squeeze().tolist())[:,0] != 0
+    true_predictions = [id2label[pred] for idx, pred in enumerate(predictions) if not is_subword[idx]]
+    true_boxes = [unnormalize_box(box, width, height) for idx, box in enumerate(token_boxes) if not is_subword[idx]]
+    # draw predictions over the image
+    draw = ImageDraw.Draw(image)
+    font = ImageFont.load_default()
+    for prediction, box in zip(true_predictions, true_boxes):
+        predicted_label = iob_to_label(prediction)
+        draw.rectangle(box, outline=label2color[predicted_label])
+        draw.text((box[0]+10, box[1]-10), text=predicted_label, fill=label2color[predicted_label], font=font)
+    return image
+
+
+title = "Invoice Information extraction using LayoutLMv3 model"
+description = "Invoice Information Extraction - We use Microsoft's LayoutLMv3 trained on Invoice Dataset to predict the Biller Name, Biller Address, Biller post_code, Due_date, GST, Invoice_date, Invoice_number, Subtotal and Total. To use it, simply upload an image or use the example image below. Results will show up in a few seconds."
+
+article="<b>References</b><br>[1] Y. Xu et al., “LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking.” 2022. <a href='https://arxiv.org/abs/2204.08387'>Paper Link</a><br>[2]  <a href='https://github.com/NielsRogge/Transformers-Tutorials/tree/master/LayoutLMv3'>LayoutLMv3 training and inference</a>" 
+
+examples =[['example1.png'],['example2.png'],['example3.png']]
+
+css = """.output_image, .input_image {height: 600px !important}"""
+
+iface = gr.Interface(fn=process_image, 
+                     inputs=gr.inputs.Image(type="pil"), 
+                     outputs=gr.outputs.Image(type="pil", label="annotated image"),
+                     title=title,
+                     description=description,
+                     article=article,
+                     examples=examples,
+                     css=css,
+                     analytics_enabled = True, enable_queue=True)
+
+iface.launch(inline=False, share=True, debug=True)